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   "cell_type": "code",
   "execution_count": 3,
   "id": "neural-board",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named 'tensorflow.examples'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
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      "\u001b[1;31mImportError\u001b[0m: No module named 'tensorflow.examples'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tes\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "reliable-sugar",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_set = input_data.read_data_sets('data/fashion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "regulated-alcohol",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0-alpha0\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(data_set.train.iamges, data_set.image.labels, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "literary-motor",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# Due to the tensorflow download issue I can not finish this lab, but I did try to splite data and train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "worldwide-consequence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000\n",
      "2000\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "running-characterization",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "artistic-ladder",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# KNN\n",
    "modeKNN = KNeighborsClassifier(n_neighbors=3)\n",
    "modeKNN.fit(x_train, y_train)\n",
    "knn_predictions = modeKNN.predict(x_test)\n",
    "print(\"DT Accuracy:   %0.1f \\t Recall: %0.1f \" % (metrics.accuracy_score(y_test, knn_predictions), metrics.recall_score(y_test, knn_predictions, average='macro')))\n",
    "print(metrics.confusion_matrix(y_test, knn_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "closed-hepatitis",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "setting an array element with a sequence.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-90-62529fdf2f57>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSGDClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax_iter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m500\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;31m# Evaluate/predict the test data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0msgd_predictions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\tensorflow\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, coef_init, intercept_init, sample_weight)\u001b[0m\n\u001b[0;32m    712\u001b[0m                          \u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlearning_rate\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    713\u001b[0m                          \u001b[0mcoef_init\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcoef_init\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mintercept_init\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mintercept_init\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 714\u001b[1;33m                          sample_weight=sample_weight)\n\u001b[0m\u001b[0;32m    715\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    716\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\tensorflow\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py\u001b[0m in \u001b[0;36m_fit\u001b[1;34m(self, X, y, alpha, C, loss, learning_rate, coef_init, intercept_init, sample_weight)\u001b[0m\n\u001b[0;32m    544\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    545\u001b[0m         X, y = check_X_y(X, y, 'csr', dtype=np.float64, order=\"C\",\n\u001b[1;32m--> 546\u001b[1;33m                          accept_large_sparse=False)\n\u001b[0m\u001b[0;32m    547\u001b[0m         \u001b[0mn_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_features\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    548\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\tensorflow\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    745\u001b[0m                     \u001b[0mensure_min_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    746\u001b[0m                     \u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 747\u001b[1;33m                     estimator=estimator)\n\u001b[0m\u001b[0;32m    748\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    749\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\tensorflow\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    520\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    521\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msimplefilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 522\u001b[1;33m                 \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    523\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    524\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\tensorflow\\lib\\site-packages\\numpy\\core\\numeric.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m    536\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    537\u001b[0m     \"\"\"\n\u001b[1;32m--> 538\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    539\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    540\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: setting an array element with a sequence."
     ]
    }
   ],
   "source": [
    "model = SGDClassifier(max_iter=500)\n",
    "model.fit(x_train, y_train)\n",
    "# Evaluate/predict the test data\n",
    "sgd_predictions = model.predict(x_test)\n",
    "print(\"SGD Accuracy:  %0.1f \\t Recall: %0.1f \" % (metrics.accuracy_score(y_test, sgd_predictions), metrics.recall_score(y_test, sgd_predictions, average='macro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "equivalent-klein",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DecisionTreeClassifier()\n",
    "model.fit(x_train, y_train)\n",
    "# Evaluate/predict the test data\n",
    "dt_predictions = model.predict(x_test)\n",
    "print(\"DT Accuracy:   %0.1f \\t Recall: %0.1f \" % (metrics.accuracy_score(y_test, dt_predictions), metrics.recall_score(y_test, dt_predictions, average='macro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sporting-ballet",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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